IMC: Free-Space Optical Neural Network With High Clockrate (Berkeley, USC, TU Berlin)


A new technical paper titled "High-clockrate free-space optical in-memory computing" was published by researchers at UC Berkeley, USC,  and TU Berlin. Abstract "The ability to process and act on data in real time is increasingly critical for applications ranging from autonomous vehicles, three-dimensional environmental sensing, and remote robotics. However, the deployment of deep neural ... » read more

Autonomous Driving: Assessment Of YOLO Algorithms (RMIT et al.)


A new technical paper titled "Advances in You Only Look Once (YOLO) algorithms for lane and object detection in autonomous vehicles" was published by RMIT University, Kyungpook National University, Deakin University and the RCA Robotics Laboratory, Royal College of Art. Abstract "Ensuring the safety and efficiency of Autonomous Vehicles (AVs) necessitates highly accurate perception, especia... » read more

Full-Chip Voltage Contrast Inference Using Deep Learning; You Only Look Once: Voltage Contrast (YOLO-VC)


Abstract: The electron beam inspection methodology for voltage contrast (VC) defects has been widely adopted in the early stages of sub-10nm logic and memory technology development, as well as in new product introductions. However, due to throughput limitations, full-chip inspection at the 300mm wafer scale remains impractical for yield ramp and production applications. To address this challeng... » read more

IBM’s Energy-Efficient NorthPole AI Unit


At this point it is well known that from an energy efficiency standpoint, the biggest bang for the back is to be found at the highest levels of abstraction. Fitting the right architecture to the task at hand i.e., an application specific architecture, will lead to benefits that are hard or impossible to claw back later in the design and implementation flow.  With the huge increase in the inter... » read more

Object Detection CNN Suitable For Edge Processors With Limited Memory


A technical paper titled “TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollers” was published by researchers at ETH Zurich. Abstract: "This paper introduces a highly flexible, quantized, memory-efficient, and ultra-lightweight object detection network, called TinyissimoYOLO. It aims to enable object detection on microcontrol... » read more

Issues And Challenges In Super-Resolution Object Detection And Recognition


If you want high performance AI inference, such as Super-Resolution Object Detection and Recognition, in your SoC the challenge is to find a solution that can meet your needs and constraints. You need inference IP that can run the model you want at high accuracy. You need inference IP that can run the model at the frame rate you want: higher frame rate = lower latency, more time for dec... » read more

Identifying PCB Defects with a Deep Learning Single-Step Detection Model


This new technical paper titled "End-to-end deep learning framework for printed circuit board manufacturing defect classification" is from researchers at École de technologie supérieure (ÉTS) in Montreal, Quebec. Abstract "We report a complete deep-learning framework using a single-step object detection model in order to quickly and accurately detect and classify the types of manufacturi... » read more

Evaluation of Thermal Imaging on Embedded GPU Platforms for Application in Vehicular Assistance Systems


Abstract "This study is focused on evaluating the real-time performance of thermal object detection for smart and safe vehicular systems by deploying the trained networks on GPU & single-board EDGE-GPU computing platforms for onboard automotive sensor suite testing. A novel large-scale thermal dataset comprising of > 35,000 distinct frames is acquired, processed, and open-sourced in challengin... » read more

Will Markets For ML Models Materialize?


Developers are spending increasing amounts of time and effort in creating machine-learning (ML) models for use in a wide variety of applications. While this will continue as the market matures, at some point some of these efforts might be seen as reinventing models over and over. Will developers of successful models ever have a marketplace in which they can sell those models as IP to other d... » read more

11 Ways To Reduce AI Energy Consumption


As the machine-learning industry evolves, the focus has expanded from merely solving the problem to solving the problem better. “Better” often has meant accuracy or speed, but as data-center energy budgets explode and machine learning moves to the edge, energy consumption has taken its place alongside accuracy and speed as a critical issue. There are a number of approaches to neural netw... » read more

← Older posts